{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
    "\n",
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive derivative of loss with respect to outputs and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('y_train: ', (49000,))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('y_val: ', (1000,))\n",
      "('y_test: ', (1000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "  print(('%s: ' % k, v.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.76984946819e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-9.\n",
    "print('Testing affine_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  5.39907127746e-11\n",
      "dw error:  1.088958711e-10\n",
      "db error:  2.41228675681e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-10\n",
    "print('Testing affine_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# ReLU layer: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.99999979802e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 5e-8\n",
    "print('Testing relu_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# ReLU layer: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.27563491363e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be around 3e-12\n",
    "print('Testing relu_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward:\n",
      "dx error:  1.1411466627e-10\n",
      "dw error:  8.16201557044e-11\n",
      "db error:  7.82672402146e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "print('Testing affine_relu_forward:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.9996027491\n",
      "dx error:  1.40215660067e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.3025458445\n",
      "dx error:  9.38467316199e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
    "print('Testing svm_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
    "print('\\nTesting softmax_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.83e-08\n",
      "W2 relative error: 3.48e-10\n",
      "b1 relative error: 6.55e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 1.00e+00\n",
      "W2 relative error: 1.00e+00\n",
      "b1 relative error: 1.35e-08\n",
      "b2 relative error: 7.76e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-3\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print('Testing initialization ... ')\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print('Testing test-time forward pass ... ')\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print('Testing training loss (no regularization)')\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "for reg in [0.0, 0.7]:\n",
    "  print('Running numeric gradient check with reg = ', reg)\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4900) loss: 2.304060\n",
      "(Epoch 0 / 10) train acc: 0.116000; val_acc: 0.094000\n",
      "(Iteration 101 / 4900) loss: 1.829613\n",
      "(Iteration 201 / 4900) loss: 1.857390\n",
      "(Iteration 301 / 4900) loss: 1.744448\n",
      "(Iteration 401 / 4900) loss: 1.420187\n",
      "(Epoch 1 / 10) train acc: 0.407000; val_acc: 0.422000\n",
      "(Iteration 501 / 4900) loss: 1.565913\n",
      "(Iteration 601 / 4900) loss: 1.700510\n",
      "(Iteration 701 / 4900) loss: 1.732213\n",
      "(Iteration 801 / 4900) loss: 1.688361\n",
      "(Iteration 901 / 4900) loss: 1.439529\n",
      "(Epoch 2 / 10) train acc: 0.497000; val_acc: 0.468000\n",
      "(Iteration 1001 / 4900) loss: 1.385772\n",
      "(Iteration 1101 / 4900) loss: 1.278401\n",
      "(Iteration 1201 / 4900) loss: 1.641580\n",
      "(Iteration 1301 / 4900) loss: 1.438847\n",
      "(Iteration 1401 / 4900) loss: 1.172536\n",
      "(Epoch 3 / 10) train acc: 0.490000; val_acc: 0.466000\n",
      "(Iteration 1501 / 4900) loss: 1.346286\n",
      "(Iteration 1601 / 4900) loss: 1.268492\n",
      "(Iteration 1701 / 4900) loss: 1.318215\n",
      "(Iteration 1801 / 4900) loss: 1.395750\n",
      "(Iteration 1901 / 4900) loss: 1.338233\n",
      "(Epoch 4 / 10) train acc: 0.532000; val_acc: 0.497000\n",
      "(Iteration 2001 / 4900) loss: 1.343165\n",
      "(Iteration 2101 / 4900) loss: 1.393173\n",
      "(Iteration 2201 / 4900) loss: 1.276734\n",
      "(Iteration 2301 / 4900) loss: 1.287951\n",
      "(Iteration 2401 / 4900) loss: 1.352778\n",
      "(Epoch 5 / 10) train acc: 0.525000; val_acc: 0.475000\n",
      "(Iteration 2501 / 4900) loss: 1.390234\n",
      "(Iteration 2601 / 4900) loss: 1.276361\n",
      "(Iteration 2701 / 4900) loss: 1.111768\n",
      "(Iteration 2801 / 4900) loss: 1.271688\n",
      "(Iteration 2901 / 4900) loss: 1.272039\n",
      "(Epoch 6 / 10) train acc: 0.546000; val_acc: 0.509000\n",
      "(Iteration 3001 / 4900) loss: 1.304489\n",
      "(Iteration 3101 / 4900) loss: 1.346667\n",
      "(Iteration 3201 / 4900) loss: 1.325510\n",
      "(Iteration 3301 / 4900) loss: 1.392728\n",
      "(Iteration 3401 / 4900) loss: 1.402001\n",
      "(Epoch 7 / 10) train acc: 0.567000; val_acc: 0.505000\n",
      "(Iteration 3501 / 4900) loss: 1.319024\n",
      "(Iteration 3601 / 4900) loss: 1.153287\n",
      "(Iteration 3701 / 4900) loss: 1.180922\n",
      "(Iteration 3801 / 4900) loss: 1.093164\n",
      "(Iteration 3901 / 4900) loss: 1.135902\n",
      "(Epoch 8 / 10) train acc: 0.568000; val_acc: 0.490000\n",
      "(Iteration 4001 / 4900) loss: 1.191735\n",
      "(Iteration 4101 / 4900) loss: 1.359396\n",
      "(Iteration 4201 / 4900) loss: 1.227283\n",
      "(Iteration 4301 / 4900) loss: 1.024113\n",
      "(Iteration 4401 / 4900) loss: 1.327583\n",
      "(Epoch 9 / 10) train acc: 0.592000; val_acc: 0.504000\n",
      "(Iteration 4501 / 4900) loss: 0.963330\n",
      "(Iteration 4601 / 4900) loss: 1.445619\n",
      "(Iteration 4701 / 4900) loss: 1.007542\n",
      "(Iteration 4801 / 4900) loss: 1.005175\n",
      "(Epoch 10 / 10) train acc: 0.611000; val_acc: 0.512000\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet()\n",
    "solver = None\n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "data['X_train']\n",
    "solver = Solver(model,data,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                lr_decay=0.95,\n",
    "                num_epochs=10, batch_size=100,\n",
    "                print_every=100)\n",
    "solver.train()\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################\\"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABMsAAAPzCAYAAACp44vsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2114d305f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Initial loss and gradient check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-6 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.30047908977\n",
      "W1 relative error: 1.48e-07\n",
      "W2 relative error: 2.21e-05\n",
      "W3 relative error: 3.53e-07\n",
      "b1 relative error: 5.38e-09\n",
      "b2 relative error: 2.09e-09\n",
      "b3 relative error: 5.80e-11\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.05211477653\n",
      "W1 relative error: 3.90e-09\n",
      "W2 relative error: 6.87e-08\n",
      "W3 relative error: 2.13e-08\n",
      "b1 relative error: 1.48e-08\n",
      "b2 relative error: 1.72e-09\n",
      "b3 relative error: 1.57e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. You will need to tweak the learning rate and initialization scale, but you should be able to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 2.363364\n",
      "(Epoch 0 / 20) train acc: 0.180000; val_acc: 0.108000\n",
      "(Epoch 1 / 20) train acc: 0.320000; val_acc: 0.127000\n",
      "(Epoch 2 / 20) train acc: 0.440000; val_acc: 0.172000\n",
      "(Epoch 3 / 20) train acc: 0.500000; val_acc: 0.184000\n",
      "(Epoch 4 / 20) train acc: 0.540000; val_acc: 0.181000\n",
      "(Epoch 5 / 20) train acc: 0.740000; val_acc: 0.190000\n",
      "(Iteration 11 / 40) loss: 0.839976\n",
      "(Epoch 6 / 20) train acc: 0.740000; val_acc: 0.187000\n",
      "(Epoch 7 / 20) train acc: 0.740000; val_acc: 0.183000\n",
      "(Epoch 8 / 20) train acc: 0.820000; val_acc: 0.177000\n",
      "(Epoch 9 / 20) train acc: 0.860000; val_acc: 0.200000\n",
      "(Epoch 10 / 20) train acc: 0.920000; val_acc: 0.191000\n",
      "(Iteration 21 / 40) loss: 0.337174\n",
      "(Epoch 11 / 20) train acc: 0.960000; val_acc: 0.189000\n",
      "(Epoch 12 / 20) train acc: 0.940000; val_acc: 0.180000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.199000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.199000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.195000\n",
      "(Iteration 31 / 40) loss: 0.075911\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.182000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.201000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.207000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.185000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.192000\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1QAAAK9CAYAAAAjXS2jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2114d10160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 1e-2\n",
    "learning_rate = 1e-2\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 166.501707\n",
      "(Epoch 0 / 20) train acc: 0.220000; val_acc: 0.116000\n",
      "(Epoch 1 / 20) train acc: 0.240000; val_acc: 0.083000\n",
      "(Epoch 2 / 20) train acc: 0.160000; val_acc: 0.104000\n",
      "(Epoch 3 / 20) train acc: 0.520000; val_acc: 0.106000\n",
      "(Epoch 4 / 20) train acc: 0.700000; val_acc: 0.131000\n",
      "(Epoch 5 / 20) train acc: 0.700000; val_acc: 0.116000\n",
      "(Iteration 11 / 40) loss: 4.414592\n",
      "(Epoch 6 / 20) train acc: 0.840000; val_acc: 0.114000\n",
      "(Epoch 7 / 20) train acc: 0.880000; val_acc: 0.108000\n",
      "(Epoch 8 / 20) train acc: 0.900000; val_acc: 0.109000\n",
      "(Epoch 9 / 20) train acc: 0.960000; val_acc: 0.114000\n",
      "(Epoch 10 / 20) train acc: 0.980000; val_acc: 0.127000\n",
      "(Iteration 21 / 40) loss: 0.261098\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.126000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.124000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.124000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.124000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Iteration 31 / 40) loss: 0.000594\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.125000\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAAK9CAYAAADWo6YTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2114d30470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "learning_rate = 1e-3\n",
    "weight_scale = 1e-1\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Inline question: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net?\n",
    "\n",
    "# Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochstic gradient descent.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than 1e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.88234703351e-09\n",
      "velocity error:  4.26928774328e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(next_w, expected_next_w))\n",
    "print('velocity error: ', rel_error(expected_velocity, config['velocity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.559978\n",
      "(Epoch 0 / 5) train acc: 0.103000; val_acc: 0.108000\n",
      "(Iteration 11 / 200) loss: 2.291087\n",
      "(Iteration 21 / 200) loss: 2.153591\n",
      "(Iteration 31 / 200) loss: 2.082693\n",
      "(Epoch 1 / 5) train acc: 0.277000; val_acc: 0.242000\n",
      "(Iteration 41 / 200) loss: 2.004171\n",
      "(Iteration 51 / 200) loss: 2.010409\n",
      "(Iteration 61 / 200) loss: 2.023753\n",
      "(Iteration 71 / 200) loss: 2.026621\n",
      "(Epoch 2 / 5) train acc: 0.352000; val_acc: 0.312000\n",
      "(Iteration 81 / 200) loss: 1.807163\n",
      "(Iteration 91 / 200) loss: 1.914256\n",
      "(Iteration 101 / 200) loss: 1.920494\n",
      "(Iteration 111 / 200) loss: 1.708877\n",
      "(Epoch 3 / 5) train acc: 0.399000; val_acc: 0.316000\n",
      "(Iteration 121 / 200) loss: 1.701111\n",
      "(Iteration 131 / 200) loss: 1.769697\n",
      "(Iteration 141 / 200) loss: 1.788899\n",
      "(Iteration 151 / 200) loss: 1.816437\n",
      "(Epoch 4 / 5) train acc: 0.427000; val_acc: 0.323000\n",
      "(Iteration 161 / 200) loss: 1.634111\n",
      "(Iteration 171 / 200) loss: 1.900044\n",
      "(Iteration 181 / 200) loss: 1.542669\n",
      "(Iteration 191 / 200) loss: 1.713051\n",
      "(Epoch 5 / 5) train acc: 0.435000; val_acc: 0.325000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 3.153778\n",
      "(Epoch 0 / 5) train acc: 0.105000; val_acc: 0.093000\n",
      "(Iteration 11 / 200) loss: 2.145874\n",
      "(Iteration 21 / 200) loss: 2.032562\n",
      "(Iteration 31 / 200) loss: 1.985848\n",
      "(Epoch 1 / 5) train acc: 0.311000; val_acc: 0.281000\n",
      "(Iteration 41 / 200) loss: 1.882354\n",
      "(Iteration 51 / 200) loss: 1.855372\n",
      "(Iteration 61 / 200) loss: 1.649133\n",
      "(Iteration 71 / 200) loss: 1.806432\n",
      "(Epoch 2 / 5) train acc: 0.415000; val_acc: 0.324000\n",
      "(Iteration 81 / 200) loss: 1.907840\n",
      "(Iteration 91 / 200) loss: 1.510681\n",
      "(Iteration 101 / 200) loss: 1.546872\n",
      "(Iteration 111 / 200) loss: 1.512046\n",
      "(Epoch 3 / 5) train acc: 0.434000; val_acc: 0.321000\n",
      "(Iteration 121 / 200) loss: 1.677301\n",
      "(Iteration 131 / 200) loss: 1.504686\n",
      "(Iteration 141 / 200) loss: 1.633253\n",
      "(Iteration 151 / 200) loss: 1.745081\n",
      "(Epoch 4 / 5) train acc: 0.460000; val_acc: 0.353000\n",
      "(Iteration 161 / 200) loss: 1.485411\n",
      "(Iteration 171 / 200) loss: 1.610416\n",
      "(Iteration 181 / 200) loss: 1.528331\n",
      "(Iteration 191 / 200) loss: 1.447238\n",
      "(Epoch 5 / 5) train acc: 0.515000; val_acc: 0.384000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABNAAAATbCAYAAABY9UgmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2143f2b128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-2,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.52468751104e-08\n",
      "cache error:  2.64779558072e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation; you should see errors less than 1e-7\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  0.207207036686\n",
      "v error:  4.20831403811e-09\n",
      "m error:  4.21496319311e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation; you should see errors around 1e-7 or less\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('v error: ', rel_error(expected_v, config['v']))\n",
    "print('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 3.476928\n",
      "(Epoch 0 / 5) train acc: 0.105000; val_acc: 0.100000\n",
      "(Iteration 11 / 200) loss: 2.036673\n",
      "(Iteration 21 / 200) loss: 2.231720\n",
      "(Iteration 31 / 200) loss: 1.986360\n",
      "(Epoch 1 / 5) train acc: 0.290000; val_acc: 0.234000\n",
      "(Iteration 41 / 200) loss: 1.965302\n",
      "(Iteration 51 / 200) loss: 1.855713\n",
      "(Iteration 61 / 200) loss: 1.988635\n",
      "(Iteration 71 / 200) loss: 1.689296\n",
      "(Epoch 2 / 5) train acc: 0.327000; val_acc: 0.298000\n",
      "(Iteration 81 / 200) loss: 1.720232\n",
      "(Iteration 91 / 200) loss: 1.671678\n",
      "(Iteration 101 / 200) loss: 1.691292\n",
      "(Iteration 111 / 200) loss: 1.899874\n",
      "(Epoch 3 / 5) train acc: 0.359000; val_acc: 0.322000\n",
      "(Iteration 121 / 200) loss: 1.601119\n",
      "(Iteration 131 / 200) loss: 1.821699\n",
      "(Iteration 141 / 200) loss: 1.633392\n",
      "(Iteration 151 / 200) loss: 1.594432\n",
      "(Epoch 4 / 5) train acc: 0.413000; val_acc: 0.368000\n",
      "(Iteration 161 / 200) loss: 1.857656\n",
      "(Iteration 171 / 200) loss: 1.542905\n",
      "(Iteration 181 / 200) loss: 1.630024\n",
      "(Iteration 191 / 200) loss: 1.569177\n",
      "(Epoch 5 / 5) train acc: 0.425000; val_acc: 0.367000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.589166\n",
      "(Epoch 0 / 5) train acc: 0.119000; val_acc: 0.146000\n",
      "(Iteration 11 / 200) loss: 2.032921\n",
      "(Iteration 21 / 200) loss: 1.897930\n",
      "(Iteration 31 / 200) loss: 1.767816\n",
      "(Epoch 1 / 5) train acc: 0.373000; val_acc: 0.318000\n",
      "(Iteration 41 / 200) loss: 1.889267\n",
      "(Iteration 51 / 200) loss: 1.682879\n",
      "(Iteration 61 / 200) loss: 1.490141\n",
      "(Iteration 71 / 200) loss: 1.623310\n",
      "(Epoch 2 / 5) train acc: 0.432000; val_acc: 0.351000\n",
      "(Iteration 81 / 200) loss: 1.507987\n",
      "(Iteration 91 / 200) loss: 1.619558\n",
      "(Iteration 101 / 200) loss: 1.495320\n",
      "(Iteration 111 / 200) loss: 1.583854\n",
      "(Epoch 3 / 5) train acc: 0.479000; val_acc: 0.366000\n",
      "(Iteration 121 / 200) loss: 1.485016\n",
      "(Iteration 131 / 200) loss: 1.539614\n",
      "(Iteration 141 / 200) loss: 1.546333\n",
      "(Iteration 151 / 200) loss: 1.654800\n",
      "(Epoch 4 / 5) train acc: 0.520000; val_acc: 0.357000\n",
      "(Iteration 161 / 200) loss: 1.601834\n",
      "(Iteration 171 / 200) loss: 1.427366\n",
      "(Iteration 181 / 200) loss: 1.495700\n",
      "(Iteration 191 / 200) loss: 1.389828\n",
      "(Epoch 5 / 5) train acc: 0.529000; val_acc: 0.371000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABNAAAATbCAYAAABY9UgmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2114e5ad30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 2450) loss: 2.354599\n",
      "(Epoch 0 / 5) train acc: 0.137000; val_acc: 0.149000\n",
      "(Iteration 11 / 2450) loss: 1.953681\n",
      "(Iteration 21 / 2450) loss: 1.906011\n",
      "(Iteration 31 / 2450) loss: 1.759756\n",
      "(Iteration 41 / 2450) loss: 1.729948\n",
      "(Iteration 51 / 2450) loss: 1.699447\n",
      "(Iteration 61 / 2450) loss: 1.707177\n",
      "(Iteration 71 / 2450) loss: 1.717275\n",
      "(Iteration 81 / 2450) loss: 1.717679\n",
      "(Iteration 91 / 2450) loss: 1.576624\n",
      "(Iteration 101 / 2450) loss: 1.740381\n",
      "(Iteration 111 / 2450) loss: 1.618682\n",
      "(Iteration 121 / 2450) loss: 1.517324\n",
      "(Iteration 131 / 2450) loss: 1.749135\n",
      "(Iteration 141 / 2450) loss: 1.672284\n",
      "(Iteration 151 / 2450) loss: 1.723303\n",
      "(Iteration 161 / 2450) loss: 1.788920\n",
      "(Iteration 171 / 2450) loss: 1.494760\n",
      "(Iteration 181 / 2450) loss: 1.727525\n",
      "(Iteration 191 / 2450) loss: 1.615380\n",
      "(Iteration 201 / 2450) loss: 1.588822\n",
      "(Iteration 211 / 2450) loss: 1.501559\n",
      "(Iteration 221 / 2450) loss: 1.535973\n",
      "(Iteration 231 / 2450) loss: 1.648610\n",
      "(Iteration 241 / 2450) loss: 1.579358\n",
      "(Iteration 251 / 2450) loss: 1.610376\n",
      "(Iteration 261 / 2450) loss: 1.668389\n",
      "(Iteration 271 / 2450) loss: 1.285441\n",
      "(Iteration 281 / 2450) loss: 1.401508\n",
      "(Iteration 291 / 2450) loss: 1.645259\n",
      "(Iteration 301 / 2450) loss: 1.592818\n",
      "(Iteration 311 / 2450) loss: 1.472625\n",
      "(Iteration 321 / 2450) loss: 1.451753\n",
      "(Iteration 331 / 2450) loss: 1.415450\n",
      "(Iteration 341 / 2450) loss: 1.512975\n",
      "(Iteration 351 / 2450) loss: 1.532234\n",
      "(Iteration 361 / 2450) loss: 1.586183\n",
      "(Iteration 371 / 2450) loss: 1.469231\n",
      "(Iteration 381 / 2450) loss: 1.486222\n",
      "(Iteration 391 / 2450) loss: 1.542808\n",
      "(Iteration 401 / 2450) loss: 1.542970\n",
      "(Iteration 411 / 2450) loss: 1.339599\n",
      "(Iteration 421 / 2450) loss: 1.543391\n",
      "(Iteration 431 / 2450) loss: 1.513100\n",
      "(Iteration 441 / 2450) loss: 1.284621\n",
      "(Iteration 451 / 2450) loss: 1.542357\n",
      "(Iteration 461 / 2450) loss: 1.542622\n",
      "(Iteration 471 / 2450) loss: 1.445221\n",
      "(Iteration 481 / 2450) loss: 1.331089\n",
      "(Epoch 1 / 5) train acc: 0.497000; val_acc: 0.473000\n",
      "(Iteration 491 / 2450) loss: 1.282859\n",
      "(Iteration 501 / 2450) loss: 1.507268\n",
      "(Iteration 511 / 2450) loss: 1.558836\n",
      "(Iteration 521 / 2450) loss: 1.444508\n",
      "(Iteration 531 / 2450) loss: 1.494040\n",
      "(Iteration 541 / 2450) loss: 1.368617\n",
      "(Iteration 551 / 2450) loss: 1.403152\n",
      "(Iteration 561 / 2450) loss: 1.534119\n",
      "(Iteration 571 / 2450) loss: 1.303954\n",
      "(Iteration 581 / 2450) loss: 1.277209\n",
      "(Iteration 591 / 2450) loss: 1.394168\n",
      "(Iteration 601 / 2450) loss: 1.540635\n",
      "(Iteration 611 / 2450) loss: 1.387256\n",
      "(Iteration 621 / 2450) loss: 1.451059\n",
      "(Iteration 631 / 2450) loss: 1.433061\n",
      "(Iteration 641 / 2450) loss: 1.482152\n",
      "(Iteration 651 / 2450) loss: 1.672916\n",
      "(Iteration 661 / 2450) loss: 1.292715\n",
      "(Iteration 671 / 2450) loss: 1.260848\n",
      "(Iteration 681 / 2450) loss: 1.621503\n",
      "(Iteration 691 / 2450) loss: 1.404492\n",
      "(Iteration 701 / 2450) loss: 1.237128\n",
      "(Iteration 711 / 2450) loss: 1.353440\n",
      "(Iteration 721 / 2450) loss: 1.462550\n",
      "(Iteration 731 / 2450) loss: 1.422084\n",
      "(Iteration 741 / 2450) loss: 1.342657\n",
      "(Iteration 751 / 2450) loss: 1.394140\n",
      "(Iteration 761 / 2450) loss: 1.307532\n",
      "(Iteration 771 / 2450) loss: 1.462813\n",
      "(Iteration 781 / 2450) loss: 1.301825\n",
      "(Iteration 791 / 2450) loss: 1.742475\n",
      "(Iteration 801 / 2450) loss: 1.318810\n",
      "(Iteration 811 / 2450) loss: 1.198774\n",
      "(Iteration 821 / 2450) loss: 1.518870\n",
      "(Iteration 831 / 2450) loss: 1.489303\n",
      "(Iteration 841 / 2450) loss: 1.316570\n",
      "(Iteration 851 / 2450) loss: 1.251101\n",
      "(Iteration 861 / 2450) loss: 1.282983\n",
      "(Iteration 871 / 2450) loss: 1.242334\n",
      "(Iteration 881 / 2450) loss: 1.372027\n",
      "(Iteration 891 / 2450) loss: 1.363292\n",
      "(Iteration 901 / 2450) loss: 1.296168\n",
      "(Iteration 911 / 2450) loss: 1.249952\n",
      "(Iteration 921 / 2450) loss: 1.304164\n",
      "(Iteration 931 / 2450) loss: 1.292421\n",
      "(Iteration 941 / 2450) loss: 1.043971\n",
      "(Iteration 951 / 2450) loss: 1.190705\n",
      "(Iteration 961 / 2450) loss: 1.283090\n",
      "(Iteration 971 / 2450) loss: 1.246623\n",
      "(Epoch 2 / 5) train acc: 0.556000; val_acc: 0.520000\n",
      "(Iteration 981 / 2450) loss: 1.350667\n",
      "(Iteration 991 / 2450) loss: 1.252506\n",
      "(Iteration 1001 / 2450) loss: 1.422484\n",
      "(Iteration 1011 / 2450) loss: 1.394563\n",
      "(Iteration 1021 / 2450) loss: 1.191698\n",
      "(Iteration 1031 / 2450) loss: 1.424986\n",
      "(Iteration 1041 / 2450) loss: 1.252609\n",
      "(Iteration 1051 / 2450) loss: 1.404886\n",
      "(Iteration 1061 / 2450) loss: 1.382343\n",
      "(Iteration 1071 / 2450) loss: 1.458254\n",
      "(Iteration 1081 / 2450) loss: 1.416839\n",
      "(Iteration 1091 / 2450) loss: 1.406581\n",
      "(Iteration 1101 / 2450) loss: 1.388713\n",
      "(Iteration 1111 / 2450) loss: 1.302219\n",
      "(Iteration 1121 / 2450) loss: 1.184911\n",
      "(Iteration 1131 / 2450) loss: 1.221663\n",
      "(Iteration 1141 / 2450) loss: 1.222922\n",
      "(Iteration 1151 / 2450) loss: 1.472358\n",
      "(Iteration 1161 / 2450) loss: 1.205860\n",
      "(Iteration 1171 / 2450) loss: 1.373454\n",
      "(Iteration 1181 / 2450) loss: 1.011333\n",
      "(Iteration 1191 / 2450) loss: 1.389616\n",
      "(Iteration 1201 / 2450) loss: 1.326715\n",
      "(Iteration 1211 / 2450) loss: 1.351658\n",
      "(Iteration 1221 / 2450) loss: 1.207418\n",
      "(Iteration 1231 / 2450) loss: 1.200027\n",
      "(Iteration 1241 / 2450) loss: 1.300580\n",
      "(Iteration 1251 / 2450) loss: 1.139737\n",
      "(Iteration 1261 / 2450) loss: 1.303387\n",
      "(Iteration 1271 / 2450) loss: 1.393407\n",
      "(Iteration 1281 / 2450) loss: 1.311053\n",
      "(Iteration 1291 / 2450) loss: 1.394309\n",
      "(Iteration 1301 / 2450) loss: 1.251233\n",
      "(Iteration 1311 / 2450) loss: 1.298124\n",
      "(Iteration 1321 / 2450) loss: 1.110674\n",
      "(Iteration 1331 / 2450) loss: 1.229541\n",
      "(Iteration 1341 / 2450) loss: 1.290125\n",
      "(Iteration 1351 / 2450) loss: 1.388201\n",
      "(Iteration 1361 / 2450) loss: 1.225293\n",
      "(Iteration 1371 / 2450) loss: 1.265576\n",
      "(Iteration 1381 / 2450) loss: 1.211209\n",
      "(Iteration 1391 / 2450) loss: 1.332727\n",
      "(Iteration 1401 / 2450) loss: 1.301199\n",
      "(Iteration 1411 / 2450) loss: 1.138552\n",
      "(Iteration 1421 / 2450) loss: 1.195878\n",
      "(Iteration 1431 / 2450) loss: 1.518587\n",
      "(Iteration 1441 / 2450) loss: 1.454967\n",
      "(Iteration 1451 / 2450) loss: 1.095657\n",
      "(Iteration 1461 / 2450) loss: 1.153223\n",
      "(Epoch 3 / 5) train acc: 0.566000; val_acc: 0.524000\n",
      "(Iteration 1471 / 2450) loss: 1.256198\n",
      "(Iteration 1481 / 2450) loss: 1.289756\n",
      "(Iteration 1491 / 2450) loss: 1.173553\n",
      "(Iteration 1501 / 2450) loss: 1.218663\n",
      "(Iteration 1511 / 2450) loss: 1.200517\n",
      "(Iteration 1521 / 2450) loss: 1.269416\n",
      "(Iteration 1531 / 2450) loss: 1.171184\n",
      "(Iteration 1541 / 2450) loss: 1.171184\n",
      "(Iteration 1551 / 2450) loss: 1.109676\n",
      "(Iteration 1561 / 2450) loss: 1.297974\n",
      "(Iteration 1571 / 2450) loss: 1.126844\n",
      "(Iteration 1581 / 2450) loss: 1.169397\n",
      "(Iteration 1591 / 2450) loss: 1.185454\n",
      "(Iteration 1601 / 2450) loss: 1.268790\n",
      "(Iteration 1611 / 2450) loss: 1.332508\n",
      "(Iteration 1621 / 2450) loss: 1.272381\n",
      "(Iteration 1631 / 2450) loss: 1.317045\n",
      "(Iteration 1641 / 2450) loss: 1.113031\n",
      "(Iteration 1651 / 2450) loss: 1.289847\n",
      "(Iteration 1661 / 2450) loss: 1.179440\n",
      "(Iteration 1671 / 2450) loss: 1.377460\n",
      "(Iteration 1681 / 2450) loss: 1.173028\n",
      "(Iteration 1691 / 2450) loss: 1.438650\n",
      "(Iteration 1701 / 2450) loss: 1.122451\n",
      "(Iteration 1711 / 2450) loss: 1.290132\n",
      "(Iteration 1721 / 2450) loss: 1.251938\n",
      "(Iteration 1731 / 2450) loss: 1.203071\n",
      "(Iteration 1741 / 2450) loss: 1.077989\n",
      "(Iteration 1751 / 2450) loss: 1.009428\n",
      "(Iteration 1761 / 2450) loss: 1.090121\n",
      "(Iteration 1771 / 2450) loss: 1.371023\n",
      "(Iteration 1781 / 2450) loss: 1.527130\n",
      "(Iteration 1791 / 2450) loss: 1.170956\n",
      "(Iteration 1801 / 2450) loss: 1.213030\n",
      "(Iteration 1811 / 2450) loss: 1.123933\n",
      "(Iteration 1821 / 2450) loss: 1.354653\n",
      "(Iteration 1831 / 2450) loss: 1.255923\n",
      "(Iteration 1841 / 2450) loss: 0.990662\n",
      "(Iteration 1851 / 2450) loss: 1.186668\n",
      "(Iteration 1861 / 2450) loss: 1.479497\n",
      "(Iteration 1871 / 2450) loss: 1.094066\n",
      "(Iteration 1881 / 2450) loss: 1.207556\n",
      "(Iteration 1891 / 2450) loss: 0.994833\n",
      "(Iteration 1901 / 2450) loss: 1.094152\n",
      "(Iteration 1911 / 2450) loss: 1.148161\n",
      "(Iteration 1921 / 2450) loss: 1.218517\n",
      "(Iteration 1931 / 2450) loss: 1.162121\n",
      "(Iteration 1941 / 2450) loss: 0.942464\n",
      "(Iteration 1951 / 2450) loss: 1.095676\n",
      "(Epoch 4 / 5) train acc: 0.614000; val_acc: 0.522000\n",
      "(Iteration 1961 / 2450) loss: 1.055813\n",
      "(Iteration 1971 / 2450) loss: 1.127538\n",
      "(Iteration 1981 / 2450) loss: 1.162758\n",
      "(Iteration 1991 / 2450) loss: 1.079798\n",
      "(Iteration 2001 / 2450) loss: 1.069712\n",
      "(Iteration 2011 / 2450) loss: 1.123507\n",
      "(Iteration 2021 / 2450) loss: 1.213189\n",
      "(Iteration 2031 / 2450) loss: 1.159273\n",
      "(Iteration 2041 / 2450) loss: 1.168598\n",
      "(Iteration 2051 / 2450) loss: 1.230290\n",
      "(Iteration 2061 / 2450) loss: 1.087844\n",
      "(Iteration 2071 / 2450) loss: 1.252096\n",
      "(Iteration 2081 / 2450) loss: 1.045404\n",
      "(Iteration 2091 / 2450) loss: 1.353505\n",
      "(Iteration 2101 / 2450) loss: 1.244269\n",
      "(Iteration 2111 / 2450) loss: 1.109386\n",
      "(Iteration 2121 / 2450) loss: 1.196736\n",
      "(Iteration 2131 / 2450) loss: 1.168004\n",
      "(Iteration 2141 / 2450) loss: 1.167371\n",
      "(Iteration 2151 / 2450) loss: 1.063737\n",
      "(Iteration 2161 / 2450) loss: 1.271425\n",
      "(Iteration 2171 / 2450) loss: 0.938628\n",
      "(Iteration 2181 / 2450) loss: 1.384508\n",
      "(Iteration 2191 / 2450) loss: 1.222243\n",
      "(Iteration 2201 / 2450) loss: 1.110948\n",
      "(Iteration 2211 / 2450) loss: 1.048861\n",
      "(Iteration 2221 / 2450) loss: 1.379033\n",
      "(Iteration 2231 / 2450) loss: 1.132657\n",
      "(Iteration 2241 / 2450) loss: 1.230346\n",
      "(Iteration 2251 / 2450) loss: 1.135386\n",
      "(Iteration 2261 / 2450) loss: 1.167963\n",
      "(Iteration 2271 / 2450) loss: 1.272533\n",
      "(Iteration 2281 / 2450) loss: 1.178768\n",
      "(Iteration 2291 / 2450) loss: 1.204257\n",
      "(Iteration 2301 / 2450) loss: 1.083601\n",
      "(Iteration 2311 / 2450) loss: 1.122557\n",
      "(Iteration 2321 / 2450) loss: 1.033564\n",
      "(Iteration 2331 / 2450) loss: 1.168405\n",
      "(Iteration 2341 / 2450) loss: 1.259742\n",
      "(Iteration 2351 / 2450) loss: 1.056686\n",
      "(Iteration 2361 / 2450) loss: 1.215827\n",
      "(Iteration 2371 / 2450) loss: 1.159073\n",
      "(Iteration 2381 / 2450) loss: 1.091414\n",
      "(Iteration 2391 / 2450) loss: 1.040679\n",
      "(Iteration 2401 / 2450) loss: 1.140913\n",
      "(Iteration 2411 / 2450) loss: 1.186290\n",
      "(Iteration 2421 / 2450) loss: 1.328186\n",
      "(Iteration 2431 / 2450) loss: 1.167871\n",
      "(Iteration 2441 / 2450) loss: 1.176221\n",
      "(Epoch 5 / 5) train acc: 0.613000; val_acc: 0.537000\n"
     ]
    }
   ],
   "source": [
    "best_model = None\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# batch normalization and dropout useful. Store your best model in the         #\n",
    "# best_model variable.                                                         #\n",
    "################################################################################\n",
    "model = FullyConnectedNet([100, 100, 100], weight_scale=5e-2,use_batchnorm=True)\n",
    "solver = Solver(model, data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3\n",
    "                  },\n",
    "                  verbose=True)\n",
    "solvers[update_rule] = solver\n",
    "solver.train()\n",
    "\n",
    "best_model = model\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Test you model\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.537\n",
      "Test set accuracy:  0.525\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
